Causal relationship between rheumatoid arthritis and thyroid dysfunction: A two-sample Mendelian randomization study

Causal relationship between rheumatoid arthritis and thyroid dysfunction: A two-sample Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal relationship between rheumatoid arthritis and thyroid dysfunction: A two-sample Mendelian randomization study Junyang Sun, Dongchu He, Jingjing Xiao, Yu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3032973/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Growing evidence has shown that Rheumatoid arthritis (RA) is associated with hyperthyroidism and hypothyroidism.However, the reciprocal cause-and-effect relationship among those three factors has not yet been substantiated. Methods We conducted a two-sample Mendelian randomization (TSMR) study with bidirectional analysis. We selected specific single nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA), hyperthyroidism, and hypothyroidism as instrumental variables. Every single nucleotide polymorphism (SNP) was derived from a genome-wide association study conducted specifically on individuals of European ancestry. For this study, the primary approach utilized to estimate the reciprocal causal relationship between rheumatoid arthritis (RA) and hyperthyroidism or hypothyroidism was the inverse-variance weighting (IVW) method. Finally, the robustness of the results was tested using sensitivity analysis and pleiotropic test. Results The utilization of the IVW method to detect rheumatoid arthritis (RA) revealed an elevated relative risk of hyperthyroidism (OR=1.33, 95% CI=1.17-1.52, P=2.407e-05), as well as a heightened risk of hypothyroidism (OR=1.29, 95% CI: 1.21-1.37, P=3.614e-16). On the flip side, it was observed that hypothyroidism might also elevate the relative risk of developing rheumatoid arthritis (OR=1.57, 95% CI=1.30-1.91, P=4.211e-06). Nevertheless, the analysis using the inverse-variance weighting (IVW) method suggested that there might not be a causal relationship between hyperthyroidism and rheumatoid arthritis (IVW: P=0.769). Finally, a sensitivity analysis was performed to assess the reliability of the results, and it indicated that no pleiotropic effects were observed, further bolstering the validity of the findings. Conclusion The findings of this study demonstrate a bidirectional causal relationship between genetic susceptibility to rheumatoid arthritis (RA) and an augmented risk of developing hypothyroidism, and vice versa. Moreover, this research establishes a positive causal relationship between genetic susceptibility to rheumatoid arthritis (RA) and an elevated risk of hyperthyroidism. However, it does not provide evidence to support a causal relationship between genetic susceptibility to hyperthyroidism and the development of RA. Rheumatoid arthritis Hyperthyroidism Hypothyroidism Genetics Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by synovitis, pannus formation and progressive destruction of articular cartilage and bone [ 1 – 3 ]. A survey conducted on the worldwide prevalence of rheumatoid arthritis (RA) indicated that the global prevalence of RA stood at 0.46% between 1980 and 2018, which was higher than the current estimated prevalence of RA in China of 0.42%[ 4 – 5 ]. The occurrence of RA is associated with significant morbidity, severe functional loss and shortened life expectancy, resulting in a serious socio-economic burden to society[ 6 ]. In addition, patients with RA have a high burden of comorbidities and often co-exist clinically with autoimmune diseases, including thyroid disease [ 7 ]. Various studies have consistently demonstrated a high prevalence of both hyperthyroidism and hypothyroidism among patients diagnosed with rheumatoid arthritis (RA). [ 8 ]. Risk factors for cardiovascular disease in RA patients also include thyroid disease. Immune system disorder and lack of autoimmune tolerance are the core of the occurrence and development of RA, which may be related to the synergistic effect of genetic susceptibility and environmental factors[ 9 ]. Hyperthyroidism and hypothyroidism are the main clinical features of autoimmune thyroid disease (AITD ). AITD are characterized as organ-specific autoimmune disorders primarily driven by a specific group of T cells. These conditions primarily arise due to immune system dysfunction, leading to the immune system attacking the thyroid gland and resulting in its impaired functionality[ 10 ]. A cohort study showed about 6%~33.8% of RA patients had thyroid diseases ,and most of them are female RA patients[ 11 ]. A recent study conducted in Iran revealed that the prevalence of thyroid dysfunction in individuals with rheumatoid arthritis (RA) was twice as high compared to those with non-inflammatory rheumatologic diseases (odds ratio [OR] = 2.16; p-value > 0.001)[ 12 ]. Additionally, the incidence of autoimmune thyroid diseases (AITD) among RA patients was found to be 2.8 times higher compared to those with non-inflammatory rheumatic diseases (OR = 2.77; p-value > 0.001)[ 12 ]. The above researches suggested a possible relationship between RA and hyperthyroidism or hypothyroidism. The advent of Genome-wide Association Studies (GWAS) has facilitated the identification of numerous genetic variations associated with various human diseases[ 13 ]. This significant progress in genetic research has laid the groundwork for further exploration in Mendelian randomization studies. Mendelian randomization (MR) utilizes genetic variations that are strongly correlated with a specific exposure as instrumental variables to evaluate the causal impact of the said exposure on study outcomes [ 14 ]. Mendelian randomization reduces the influence of confounding factors and ethics in traditional epidemiological studies, so it is widely used to infer causal relationships in epidemiological diseases[ 15 , 16 ]. Even though some observational researches have suggested that RA may be associated with hyperthyroidism and hypothyroidism, they cannot explain the potential causal relationship. Consequently, we employed Mendelian randomization analysis to investigate the reciprocal causal relationship between rheumatoid arthritis (RA) and both hyperthyroidism and hypothyroidism. This research aims to establish a theoretical foundation for the diagnosis and treatment of RA patients with comorbid thyroid dysfunction, ultimately enhancing patient prognosis. 2. Materials and methods 2.1 Study design and data sources In the European population, a bidirectional Mendelian randomization (MR) analysis was employed to examine the causal association between rheumatoid arthritis (RA) and thyroid dysfunction. Single nucleotide polymorphisms (SNPs) were utilized as instrumental variables (IVs). We selected specific single nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA), hyperthyroidism, and hypothyroidism as instrumental variables (IVs). In a causally positive relationship, exposure was RA and the outcome was hyperthyroidism and hypothyroidism; Whereas in the reverse causality, the exposure was hyperthyroidism and hypothyroidism and the result was RA. SNPs were employed as instrumental variables (IVs) to simulate the impact of exposure factors on disease susceptibility[ 15 ]. To enhance the precision of estimating the causal effect, the fulfillment of three fundamental assumptions - relevance, independence, and exclusivity - between the instrumental variables (IVs) is required prior to the study[ 17 ]. Figure 1 displays both the key assumptions and the comprehensive flow chart of the bidirectional Mendelian randomization (MR) investigation. The genome-wide association study (GWAS) data for rheumatoid arthritis (RA) was obtained from the IEU database's Genome-wide Association Study (GWAS) platform, accessible at https://gwas.mrcieu.ac.uk/ , including 58,284 samples(14,361cases, 43,923 controls ) and 13,108,512 SNPs. Hyperthyroidism and hypothyroidism were chosen as exposures.GWAS summary statistics for hypothyroidism and hyperthyroidism were extracted from the the IEU database ( https://gwas.mrcieu.ac.uk/).Th e data for hypothyroidism were obtained from 20,2617 samples (22,997 cases, 175,475 controls) and 16,380,353 SNPs, while the data for hyperthyroidism came from 20,2617 samples (962 cases, 172,976 controls) and 16,380,189 SNPs. In this Mendelian randomization (MR) study, Table 1 presents comprehensive information regarding the summary-level data from genome-wide association studies (GWAS) conducted on rheumatoid arthritis (RA), hypothyroidism, and hyperthyroidism. 2.2 Genetic IV selection In this research, the effective IVs based on three assumptions were screened (Fig. 1 ). First, in order to ensure that the instrumental variables were significantly associated with the exposure factors, we implemented a genome-wide significance threshold (P < 5e-8) to identify the instrumental variables for Mendelian randomization (MR) analysis. We set the linkage disequilibrium (LD) r 2 to < 0.001 and the length of the region of LD to 10,000KB to eliminate linkage disequilibrium bias between each SNP, thus ensuring independence between genetic tools[ 18 ]. Secondary, to eliminate IVs that could potentially confound the association between exposure and outcome, we conducted a search for phenotypes associated with each SNP using the phenoscanner database ( http://www.phenoscanner.medschl.cam.ac.uk/ )[ 19 ]. IVs found to be confounding factors were removed from further analysis. Finally, We calculated the F-value for each SNP and assessed the IVs based on their F-values to determine if they were weak. In order to mitigate bias arising from weak instrumental variables, we only retained IVs with an F-value > 10[ 18 ]. To ensure proper matching between exposure and outcome IVs, it is vital to coordinate the data of both the exposure and outcome variables. This coordination ensures that the impact of the IVs on the exposure or outcome aligns with the same allele. 2.3 Statistical analyses For this study, we employed the inverse-variance weighting (IVW) method as the primary approach to estimate a bidirectional causal relationship between RA and thyroid dysfunction. Additionally, to further assess the causal associations, we utilized additional methods including MR-Egger, weighted median, weighted model, and simple model. These approaches provided complementary analyses for evaluating causal relationships[ 20 ]. IVW method used wald ratio method to carry out association of a single SNP firs. A fixed-effect model or a random-effects model was employed for the analysis. These two models were considered in order to account for potential heterogeneity across different studies or samples. And then a fixed-effect model or a random-effects model was selected to carry out a meta-summary of multiple-locus effects, regardless of the existence of the intercept term. And it is considered that SNP has no pleiotropy[ 21 ]. MR-Egger considers the presence of an intercept term, which was originally utilized to evaluate "publication bias" in meta-analysis for assessing horizontal pleiotropy. This intercept term is now incorporated to account for potential bias arising from pleiotropic effects in the analysis[ 22 ]. By combining data from multiple genetic variations into a single causal estimate, the weighted median method serves as a supplementary approach to the MR Egger regression method. This method enhances the robustness of the analysis by providing a more comprehensive and reliable estimate of causality[ 23 ]. 2.4 Sensitivity analyses In order to ensure the reliability of establishing a two-way causal relationship between RA, hyperthyroidism and hypothyroidism, a series of sensitivity analyses were conducted. Firstly, heterogeneity was evaluated using Cochran's Q test. The primary purpose of the heterogeneity test is to examine the differences among each IV. The huge difference between the IVs (Q-p value < 0.05). Due to the significant heterogeneity observed among these independent variables (IVs), the random effects model was directly employed to estimate the effect size of MR. Next, the MR-Egger intercept allows for the assessment of horizontal pleiotropy, with a p-value > 0.05 indicating no substantial evidence of such pleiotropic effects[ 24 ]. Furthermore, a leave-one-out sensitivity analysis was conducted to identify and exclude individual IVs that exerted a significant independent influence on the MR results[ 25 ]. The whole analyses were completed by the R software (version 4.2.3; R Foundation for Statistical Computing, 2023), R Studio(version, 2023.03.0–386) and R package TwoSampleMR. Table 1 Details of the GWAS summary-level data. Traits N case N control N SNP Population Data accession address RA 14,361 43,923 13,108,512 European https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90013534 hyperthyroidism 962 172,976 16,380,189 European https://gwas.mrcieu.ac.uk/datasets/finn-b-AUTOIMMUNE_HYPERTHYROIDISM hypothyroidism 22,997 175,475 16,380,353 European https://gwas.mrcieu.ac.uk/datasets/finn-b-E4_HYTHY_AI_STRICT 3. Results 3.1 Effect of RA on hyperthyroidism and hypothyroidism The effects of each SNP in RA on hyperthyroidism and hypothyroidism can be found in Table 3 , Fig. 2 A, B and Fig. 3 A, B. The MR results indicated that there is a causal effect between RA on hyperthyroidism or hypothyroidism. The utilization of the IVW method to detect RA revealed an elevated relative risk of hyperthyroidism (OR = 1.33, 95% CI = 1.17–1.52, P = 2.407e-05), as well as a heightened risk of hypothyroidism (OR = 1.29, 95% CI: 1.21–1.37, P = 3.614e-16) (Table 2 , Fig. 2 A, B and Fig. 3 A, B). Afterward, Cochran's Q test was employed to evaluate the robustness of the Mendelian randomization (MR) results,which revealed a significant difference (P < 0.05) (Table 3 ). Moreover, to mitigate heterogeneity, a random effects model was employed to estimate the effect size in the MR analysis. The findings from the MR-Egger intercept test indicated that the MR analysis was not influenced by potential horizontal pleiotropy (P > 0.05)(Table 3 ). n addition, we performed a "leave-one-out analysis" to assess the individual impact of each SNP on the overall effect of RA, hyperthyroidism, or hypothyroidism. The results indicated that the overall effect estimates of RA, hyperthyroidism, and hypothyroidism remain relatively stable and are not driven by any single SNP in the analysis(Fig. 2 C and Fig. 3 C). 3.2 Effect of hyperthyroidism and hypothyroidism on RA To investigate the causal relationships between hyperthyroidism or hypothyroidism and RA, we conducted a reverse MR analysis. The analysis suggested that there might not be a causal relationship between hyperthyroidism and rheumatoid arthritis (IVW: P = 0.769) (Table 4 ). On the flip side, it was observed that hypothyroidism might elevate the relative risk of developing rheumatoid arthritis (OR = 1.57, 95% CI = 1.30–1.91, P = 4.211e-06) (Table 4 , Fig. 4 A, B). For hypothyroidism, sensitivity analyses were conducted, and the findings indicated that the reverse MR analysis remained unaffected by any heterogeneity issues ( P > 0.05) (Table 5 ). No evidence of pleiotropic effects was observed ( P > 0.05) (Table 5 ). Through the implementation of a “leave-one-out analysis”, we discovered that no individual SNP exhibited a significant independent impact on the susceptibility to hypothyroidism and rheumatoid arthritis(Fig. 4 C). In the MR analysis, we evaluated the effects of genetically predicted rheumatoid arthritis (RA) on the likelihood of developing hyperthyroidism and hypothyroidism. Table 2 Effects of genetically predicted RA on the likelihood of developing hyperthyroidism and hypothyroidism in the MR analysis Exposure Outcome nSNP MR methods P -value OR 95%CI RA hyperthyroidism 82 MR Egger 1.381e-01 1.16 0.95–1.42 WM 1.670e-01 1.12 0.95–1.32 IVW 2.407e-05 1.33 1.17–1.52 Simple mode 3.777e-01 1.27 0.75–2.17 Weighted mode 2.754e-01 0.91 0.76–1.08 RA hypothyroidism 82 MR Egger 7.742e-07 1.29 1.18–1.42 WM 9.363e-26 1.25 1.20–1.31 IVW 3.614e-16 1.29 1.21–1.37 Simple mode 1.318e-03 1.23 1.09–1.39 Weighted mode 3.450e-08 1.24 1.16–1.33 MR, Mendelian randomization analysis; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted; WM, Weighted median; RA, Rheumatoid arthritis Table 3 Sensitivity analysis of RA with hyperthyroidism and hypothyroidism by different analysis methods Exposure Outcome MR methods Cochran Q statistic Heterogeneity P Pleiotropy P RA hyperthyroidism MR Egger 266.978 5.721e-22 0.083 IVW 277.242 2.407e-05 RA hypothyroidism MR Egger 932.129 2.446e-145 0.972 IVW 932.143 3.614e-16 MR, Mendelian randomization analysis; IVW, Inverse variance weighted; RA, Rheumatoid arthritis Table 4 Effects of genetically predicted hyperthyroidism and hypothyroidism on the likelihood of developing RA in the MR analysis Exposure Outcome nSNP MR methods P -value OR 95%CI hyperthyroidism RA 6 MR Egger 0.511 0.58 0.13–2.55 WM 0.211 0.96 0.91–1.02 IVW 0.769 0.93 0.56–1.53 Simple mode 0.231 0.89 0.75–1.05 Weighted mode 0.184 0.96 0.91–1.01 hypothyroidism RA 48 MR Egger 2.350e-03 2.20 1.36–3.56 WM 8.494e-02 1.07 0.99–145 IVW 4.211e-06 1.57 1.30–1.91 Simple mode 4.320e-01 1.04 0.94–1.14 Weighted mode 2.802e-01 1.04 0.97–1.11 MR, Mendelian randomization analysis; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted; WM, Weighted median; RA, Rheumatoid arthritis Table 5 Sensitivity analysis of hyperthyroidism and hypothyroidism with RA by different analysis methods Exposure Outcome MR methods Cochran Q statistic Heterogeneity P Pleiotropy P hyperthyroidism RA MR Egger 970.164 1.043e-208 0.542 IVW 1077.486 0.542 hypothyroidism RA MR Egger 1196.550 1.697e-220 0.972 IVW 1254.729 0.141 MR, Mendelian randomization analysis; IVW, Inverse variance weighted; RA, Rheumatoid arthritis 4. Discussion The MR method was utilized to explore the bidirectional causal association between RA and thyroid dysfunction. In the European population, we identified a reciprocal causal relationship between RA and hypothyroidism. Specifically, we found that the genetic predisposition to RA is linked to an elevated risk of hypothyroidism, and conversely, individuals with hypothyroidism have an increased susceptibility to RA. Furthermore, we observed a positive causal association between RA and hyperthyroidism. Nevertheless, the reverse MR analysis yielded inconclusive results, suggesting that there is no significant causal relationship between hyperthyroidism and RA. From our observations in clinical practice, we have identified thyroid disease as a prevalent comorbidity in patients diagnosed with RA. Previous observational studies demonstrated that individuals with RA who also had hypothyroidism, increased disease activity and joint tenderness, and correction of hypothyroidism in individuals with RA can significantly improve the disease activity of patients with RA[ 26 ]. Likewise, a prospective cohort study revealed that female patients with RA had a three-fold higher incidence of hypothyroidism compared to the general population[ 27 ]. This co-occurrence of hypothyroidism in female RA patients was found to be associated with a heightened risk of cardiovascular diseases (CVD)[ 27 ]. Recently, a meta-analysis from China determined that individuals diagnosed with RA exhibited a heightened susceptibility to developing thyroid disorders, especially hypothyroidism, which was 2.25 times higher than non-RA patients; And the increased risk of hyperthyroidism was only secondary, which was 1.65 times higher than that of non-RA patients[ 28 ]. However, these studies could not explain a causal effect between RA and thyroid dysfunction, they provided sufficient evidence for an association between RA and hyperthyroidism and hypothyroidism. Through Mr.'s research, we have demonstrated that RA may increase the incidence of hyperthyroidism and hypothyroidism, and hypothyroidism may also increase the incidence of RA, which strengthens and enriches the findings of these previous observational investigations. Mendelian randomization (MR) employs genetic variations that have strong associations with a specific exposure as instrumental variables to assess the causal effects of the exposure on diverse study outcomes[ 16 , 29 ]. In our MR study, we initially chose SNPs that exhibited strong associations with the exposure factors. The selection of these SNPs was based on the three fundamental hypotheses of association, independence, and exclusivity. Five different analysis methods are used for MR analysis. The utilization of the IVW method to detect RA revealed an elevated relative risk of hyperthyroidism (OR = 1.33, 95% CI = 1.17–1.52, P = 2.407e-05), as well as a heightened risk of hypothyroidism (OR = 1.29, 95% CI: 1.21–1.37, P = 3.614e-16). On the flip side, it was observed that hypothyroidism might also elevate the relative risk of developing rheumatoid arthritis (OR = 1.57, 95% CI = 1.30–1.91, P = 4.211e-06). The results deduced by the other four methods accorded with the IVW analysis method. We also performed a sensitivity analysis, and heterogeneity testing revealed significant heterogeneity between each instrumental variable. However, it does not affect the final analysis results. It is worth noting that the MR Egger method suggests that multiple IVs do not have horizontal pleiotropy, so the research results may be less affected by gene pleiotropy.Through the above methods, we ultimately consider that the genetic susceptibility of RA is linked to a higher chance of developing hypothyroidism, and vice versa. The genetic susceptibility of RA is linked to a higher chance of developing hyperthyroidism, but hyperthyroidism seems to have no causal effect on RA. Graves' disease (GD) is a prevalent cause of hyperthyroidism, and research has indicated that 16.7% of individuals with GD also have another autoimmune disease[ 30 ]. Among these cases, rheumatoid arthritis (RA) has a prevalence rate of 1.9%[ 30 ]. In an Asian population, a Mendelian randomization study revealed a reciprocal causal relationship between GD and RA[ 31 ]. Due to the possibility that Mendelian randomization analysis may be influenced by factors such as race or region, further research is needed on the causality between hyperthyroidism and RA based on different races or regions. Additionally, the association between RA and thyroid diseases may have some other potential pathogenic mechanisms. Hyperthyroidism and hypothyroidism are mainly caused by AITD. There exists a correlation between RA and autoimmune thyroid diseases (AITD), yet the underlying causes of their co-occurrence remain unclear. Genetic variations in the human leukocyte antigen (HLA) system, cytotoxic T lymphocyte-associated protein 4 (CTLA-4), and protein tyrosine phosphatase non-receptor 22 (PTPN22) genes have been found to be linked to an increased susceptibility to autoimmune thyroid diseases (AITD)[ 8 , 32 , 33 ]. CTLA4 plays a negative regulatory role in T lymphocyte immune response[ 34 ]. Common allelic variations in CTLA-4 expression levels are major determinants of susceptibility to autoimmune diseases such as autoimmune thyroid disease and RA[ 32 ]. PTPN22 is associated with TCR signaling and participates in regulating CBL function in T cell receptor signaling pathways[ 35 ]. Mutations in this gene can alter TCR regulation and T cell activation, closely related to the increased risk of various autoimmune diseases, including RA and GD. Recent findings have indicated that the expression of CXCL10 significantly rises in serum and/or tissues of organ-specific autoimmune diseases like GD and RA[ 10 ]. The production of IFN-γand TNF-αin thyroid tissue may be linked to the recruitment of Th1 lymphocytes, triggering the release of CXCL10 by thyroid cells, binding to CXCR3 on the surface of Th1 lymphocytes, and promoting the recruitment of Th1 lymphocytes into the thyroid, secreting IFN-γ and TNF-α, thus forming an amplified feedback loop, resulting in the persistence of the disease[ 36 – 37 ]. Similarly, many chemokines, including CXCL10, are present in the joints of RA, promote lymphocyte recruitment into the synovium, and are involved in germinal center formation[ 38 ]. Therefore, CXCL10 can be used as a therapeutic target to prevent immune cells from recruiting to tissues, which needs to be further studied, and provides a new therapeutic strategy for RA and thyroid disease comorbidity. Our research has some advantages. Firstly, the present analysis employing MR is grounded in GWAS, leveraging genetic data for causal inference purposes. Compared with traditional epidemiological methods, MR reduces the impact of confounding factors, reverse causality, and ethical ethics. Secondly, we employed diverse approaches for Mendelian randomization analysis and sensitivity analysis to ensure the robustness and validity of the final outcomes. Significantly, we have established a reciprocal causal association between RA and hypothyroidism, offering novel perspectives for clinicians in terms of RA screening and management strategies. Certainly, there are certain limitations in this study. Firstly, the inclusion of SNPs in this study was limited to European populations, thus requiring further confirmation to establish the causal relationship between RA and hyperthyroidism and hypothyroidism in other racial or regional contexts. Secondly, due to the limitation of GWAS pooled data, it is impossible to perform stratified analysis according to factors such as disease course and severity. Conclusions The evidence we provide suggests that the genetic susceptibility of RA is linked to the increasing risk of hyperthyroidism and hypothyroidism. Conversely, it is possible that genetic predisposition to hypothyroidism is linked to a higher risk of developing RA, while there may not be a causal association between genetic susceptibility to hyperthyroidism and an increased risk of RA. As a result, it is crucial to enhance screening and management of thyroid disorders in individuals with RA to enhance patient prognosis. Declarations Ethics approval and consent to participate The data utilized in this study were exclusively sourced from publicly available databases. All the data mentioned can be accessed and freely downloaded from the provided website. As a result of the re-evaluation of previously gathered and published data, no further ethics approval was required. Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are available in the the IEU database (https://gwas.mrcieu.ac.uk/).The data access links for rheumatoid arthritis (RA), hypothyroidism, and hyperthyroidism can be found in Table 1. Competing interests All authors declare no conflict of interest. Funding This research was supported by the Natural Science Foundation of China （ 81873153，81273905). Author’s contributions Junyang Sun was responsible for the research design and writing of the paper. Jingjing Xiao oversaw the collection and organization of data. Yu Wang conducted the analysis and visualization of the results. Dongchu He conducted the research quality assessment. All authors made significant contributions to the article and have approved the final submitted version. Acknowledgements We thank all those who contributed and collected data for this work. References Aletaha D, Smolen JS. Diagnosis and Management of Rheumatoid Arthritis: A Review. 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Antonelli A, Ferrari SM, Giuggioli D, Ferrannini E, Ferri C, Fallahi P. Chemokine (C-X-C motif) ligand (CXCL)10 in autoimmune diseases. Autoimmun Rev. 2014;13(3):272-280. Ferrari SM, Paparo SR, Ragusa F, et al. Chemokines in thyroid autoimmunity. Best Pract Res Clin Endocrinol Metab. 2023;37(2):101773. Zhong H, Xu LL, Bai MX, Su Y. Beijing Da Xue Xue Bao Yi Xue Ban. 2021;53(6):1026-1031. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3032973","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":215965998,"identity":"7bbe70d2-1f9b-43c6-a6d5-ddca419b654f","order_by":0,"name":"Junyang Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJACAwjFfODABwMbBjYStLAlPpxRkEacFijgMTbm+XCYsDr5GbkHinl3HJY3519gJsFjcN6eT7r5AcOPim24HXUjL8GY98xhw50zHqRJSBjcTmyTOWbA2HPmNm4tEjkGxrxthxk33DhwTMLA4HYCm0SCATNjG24t8jMgWuw33DjYBlR8zp5NIv0DXi0MNyBaEjecb2Y2OGBwgLENaC9eLQZn3hgYzm1LT95wg43xYYNBciJQS8FBfH6Rb88xM3jbZm274fz5D4f//LGzl5+RvvHBjwo8DgNGITAqmxkYJBIQQgfwqQcC5gcMDHUMDPyE1I2CUTAKRsGIBQAwr1tQvrVeIQAAAABJRU5ErkJggg==","orcid":"","institution":"Hubei University of Traditional Chinese Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Junyang","middleName":"","lastName":"Sun","suffix":""},{"id":215965999,"identity":"c6371f8b-236a-4b3c-8e2c-39b204c04251","order_by":1,"name":"Dongchu He","email":"","orcid":"","institution":"General Hospital of Central Theatre Command","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongchu","middleName":"","lastName":"He","suffix":""},{"id":215966000,"identity":"897d43fb-04e6-4289-b15b-0e48a529520f","order_by":2,"name":"Jingjing Xiao","email":"","orcid":"","institution":"General Hospital of Central Theatre Command","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Xiao","suffix":""},{"id":215966001,"identity":"65bb1413-3198-4600-b592-c02ef4dbe758","order_by":3,"name":"Yu Wang","email":"","orcid":"","institution":"Taihe Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-06-07 08:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3032973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3032973/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39856840,"identity":"ece3e927-26cc-44ec-a2eb-6af92b4286ed","added_by":"auto","created_at":"2023-07-11 14:15:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":455549,"visible":true,"origin":"","legend":"\u003cp\u003eImportant assumptions underlying the Mendelian randomization study include: Assumption 1: Association hypothesis(The hypothesis that there is a strong association between genetic variation and exposure factors) ; Assumption 2: Independence hypothesis (Genetic variation is not associated with confounding factors that affect “exposure and outcomes”); Assumption 3: Exclusivity hypothesis(Genetic variation can only contribute to outcome through exposure and not through other pathways).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3032973/v1/608bef502dbf7b6b8d2762c9.png"},{"id":39855407,"identity":"da520ed9-f4fa-454e-b6e6-6068ac14e199","added_by":"auto","created_at":"2023-07-11 14:07:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153470,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of Mendelian Randomization (MR) estimate causality between rheumatoid arthritis (RA) and hyperthyroidism. (A) A forest plot depicting the association between SNPs linked toRA and their respective influence on the risk of hyperthyroidism. (B)A scatter plot illustrating the correlation between SNPs associated with RA and their respective impact on the risk of hyperthyroidism. (C) The \"leave-one-out\" plots show that no single SNP was found to show a significant independent effect on susceptibility to hyperthyroidism and RA. SNPs, single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3032973/v1/1b9bf364a6aacaca341c9230.png"},{"id":39858090,"identity":"8f32a306-13b5-4750-81e2-370c95006073","added_by":"auto","created_at":"2023-07-11 14:23:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":588230,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of Mendelian randomization (MR) estimates of the causal relationship between rheumatoid arthritis (RA) and hypothyroidism. (A) A forest plot depicting the association between SNPs linked to RA and their respective influence on the risk of hypothyroidism. (B)A scatter plot illustrating the correlation between SNPs associated with RA and their respective impact on the risk of hypothyroidism. (C) The \"leave-one-out\" plots show that no single SNP was found to show a significant independent effect on susceptibility to hypothyroidism and RA. SNPs, single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3032973/v1/34c9a728e780e39f3bfac2fe.png"},{"id":39855404,"identity":"41457dd3-ad75-4ea7-b882-fe2707b66d55","added_by":"auto","created_at":"2023-07-11 14:07:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116995,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of Mendelian randomization (MR) estimates of the causal relationship between rheumatoid arthritis (RA) and hypothyroidism. (A) A forest plot depicting the association between SNPs linked to hypothyroidism and their respective influence on the risk of RA. (B)A scatter plot illustrating the correlation between SNPs associated with hypothyroidism and their respective impact on the risk of RA. (C) The \"leave-one-out\" plots show that no single SNP was found to show a significant independent effect on susceptibility to RA and hypothyroidism. SNPs, single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3032973/v1/cd738cc338c3758b4bd61f54.png"},{"id":46686430,"identity":"b0d3a19d-0f8a-461b-9906-ee230897c93b","added_by":"auto","created_at":"2023-11-18 02:37:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3032973/v1/ecdc967e-da1c-4280-b8f5-5f47d34d8854.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between rheumatoid arthritis and thyroid dysfunction: A two-sample Mendelian randomization study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by synovitis, pannus formation and progressive destruction of articular cartilage and bone [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A survey conducted on the worldwide prevalence of rheumatoid arthritis (RA) indicated that the global prevalence of RA stood at 0.46% between 1980 and 2018, which was higher than the current estimated prevalence of RA in China of 0.42%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The occurrence of RA is associated with significant morbidity, severe functional loss and shortened life expectancy, resulting in a serious socio-economic burden to society[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, patients with RA have a high burden of comorbidities and often co-exist clinically with autoimmune diseases, including thyroid disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Various studies have consistently demonstrated a high prevalence of both hyperthyroidism and hypothyroidism among patients diagnosed with rheumatoid arthritis (RA). [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Risk factors for cardiovascular disease in RA patients also include thyroid disease. Immune system disorder and lack of autoimmune tolerance are the core of the occurrence and development of RA, which may be related to the synergistic effect of genetic susceptibility and environmental factors[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHyperthyroidism and hypothyroidism are the main clinical features of autoimmune thyroid disease (AITD ). AITD are characterized as organ-specific autoimmune disorders primarily driven by a specific group of T cells. These conditions primarily arise due to immune system dysfunction, leading to the immune system attacking the thyroid gland and resulting in its impaired functionality[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A cohort study showed about 6%~33.8% of RA patients had thyroid diseases ,and most of them are female RA patients[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A recent study conducted in Iran revealed that the prevalence of thyroid dysfunction in individuals with rheumatoid arthritis (RA) was twice as high compared to those with non-inflammatory rheumatologic diseases (odds ratio [OR]\u0026thinsp;=\u0026thinsp;2.16; p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the incidence of autoimmune thyroid diseases (AITD) among RA patients was found to be 2.8 times higher compared to those with non-inflammatory rheumatic diseases (OR\u0026thinsp;=\u0026thinsp;2.77; p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The above researches suggested a possible relationship between RA and hyperthyroidism or hypothyroidism.\u003c/p\u003e \u003cp\u003eThe advent of Genome-wide Association Studies (GWAS) has facilitated the identification of numerous genetic variations associated with various human diseases[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This significant progress in genetic research has laid the groundwork for further exploration in Mendelian randomization studies. Mendelian randomization (MR) utilizes genetic variations that are strongly correlated with a specific exposure as instrumental variables to evaluate the causal impact of the said exposure on study outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Mendelian randomization reduces the influence of confounding factors and ethics in traditional epidemiological studies, so it is widely used to infer causal relationships in epidemiological diseases[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Even though some observational researches have suggested that RA may be associated with hyperthyroidism and hypothyroidism, they cannot explain the potential causal relationship. Consequently, we employed Mendelian randomization analysis to investigate the reciprocal causal relationship between rheumatoid arthritis (RA) and both hyperthyroidism and hypothyroidism. This research aims to establish a theoretical foundation for the diagnosis and treatment of RA patients with comorbid thyroid dysfunction, ultimately enhancing patient prognosis.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and data sources\u003c/h2\u003e \u003cp\u003eIn the European population, a bidirectional Mendelian randomization (MR) analysis was employed to examine the causal association between rheumatoid arthritis (RA) and thyroid dysfunction. Single nucleotide polymorphisms (SNPs) were utilized as instrumental variables (IVs). We selected specific single nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA), hyperthyroidism, and hypothyroidism as instrumental variables (IVs). In a causally positive relationship, exposure was RA and the outcome was hyperthyroidism and hypothyroidism; Whereas in the reverse causality, the exposure was hyperthyroidism and hypothyroidism and the result was RA. SNPs were employed as instrumental variables (IVs) to simulate the impact of exposure factors on disease susceptibility[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To enhance the precision of estimating the causal effect, the fulfillment of three fundamental assumptions - relevance, independence, and exclusivity - between the instrumental variables (IVs) is required prior to the study[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays both the key assumptions and the comprehensive flow chart of the bidirectional Mendelian randomization (MR) investigation.\u003c/p\u003e \u003cp\u003eThe genome-wide association study (GWAS) data for rheumatoid arthritis (RA) was obtained from the IEU database's Genome-wide Association Study (GWAS) platform, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, including 58,284 samples(14,361cases, 43,923 controls ) and 13,108,512 SNPs. Hyperthyroidism and hypothyroidism were chosen as exposures.GWAS summary statistics for hypothyroidism and hyperthyroidism were extracted from the the IEU database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/).Th\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/).Th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee data for hypothyroidism were obtained from 20,2617 samples (22,997 cases, 175,475 controls) and 16,380,353 SNPs, while the data for hyperthyroidism came from 20,2617 samples (962 cases, 172,976 controls) and 16,380,189 SNPs. In this Mendelian randomization (MR) study, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents comprehensive information regarding the summary-level data from genome-wide association studies (GWAS) conducted on rheumatoid arthritis (RA), hypothyroidism, and hyperthyroidism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Genetic IV selection\u003c/h2\u003e \u003cp\u003eIn this research, the effective IVs based on three assumptions were screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). First, in order to ensure that the instrumental variables were significantly associated with the exposure factors, we implemented a genome-wide significance threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5e-8) to identify the instrumental variables for Mendelian randomization (MR) analysis. We set the linkage disequilibrium (LD) r\u003csup\u003e2\u003c/sup\u003e to \u0026lt;\u0026thinsp;0.001 and the length of the region of LD to 10,000KB to eliminate linkage disequilibrium bias between each SNP, thus ensuring independence between genetic tools[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Secondary, to eliminate IVs that could potentially confound the association between exposure and outcome, we conducted a search for phenotypes associated with each SNP using the phenoscanner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. IVs found to be confounding factors were removed from further analysis. Finally, We calculated the F-value for each SNP and assessed the IVs based on their F-values to determine if they were weak. In order to mitigate bias arising from weak instrumental variables, we only retained IVs with an F-value\u0026thinsp;\u0026gt;\u0026thinsp;10[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To ensure proper matching between exposure and outcome IVs, it is vital to coordinate the data of both the exposure and outcome variables. This coordination ensures that the impact of the IVs on the exposure or outcome aligns with the same allele.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e \u003cp\u003eFor this study, we employed the inverse-variance weighting (IVW) method as the primary approach to estimate a bidirectional causal relationship between RA and thyroid dysfunction. Additionally, to further assess the causal associations, we utilized additional methods including MR-Egger, weighted median, weighted model, and simple model. These approaches provided complementary analyses for evaluating causal relationships[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. IVW method used wald ratio method to carry out association of a single SNP firs. A fixed-effect model or a random-effects model was employed for the analysis. These two models were considered in order to account for potential heterogeneity across different studies or samples. And then a fixed-effect model or a random-effects model was selected to carry out a meta-summary of multiple-locus effects, regardless of the existence of the intercept term. And it is considered that SNP has no pleiotropy[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. MR-Egger considers the presence of an intercept term, which was originally utilized to evaluate \"publication bias\" in meta-analysis for assessing horizontal pleiotropy. This intercept term is now incorporated to account for potential bias arising from pleiotropic effects in the analysis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By combining data from multiple genetic variations into a single causal estimate, the weighted median method serves as a supplementary approach to the MR Egger regression method. This method enhances the robustness of the analysis by providing a more comprehensive and reliable estimate of causality[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sensitivity analyses\u003c/h2\u003e \u003cp\u003eIn order to ensure the reliability of establishing a two-way causal relationship between RA, hyperthyroidism and hypothyroidism, a series of sensitivity analyses were conducted. Firstly, heterogeneity was evaluated using Cochran's Q test. The primary purpose of the heterogeneity test is to examine the differences among each IV. The huge difference between the IVs (Q-p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Due to the significant heterogeneity observed among these independent variables (IVs), the random effects model was directly employed to estimate the effect size of MR. Next, the MR-Egger intercept allows for the assessment of horizontal pleiotropy, with a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating no substantial evidence of such pleiotropic effects[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, a leave-one-out sensitivity analysis was conducted to identify and exclude individual IVs that exerted a significant independent influence on the MR results[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe whole analyses were completed by the R software (version 4.2.3; R Foundation for Statistical Computing, 2023), R Studio(version, 2023.03.0\u0026ndash;386) and R package TwoSampleMR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the GWAS summary-level data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN case\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eData accession address\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43,923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,108,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90013534\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90013534\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehyperthyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172,976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,380,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-AUTOIMMUNE_HYPERTHYROIDISM\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/finn-b-AUTOIMMUNE_HYPERTHYROIDISM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,380,353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/finn-b-E4_HYTHY_AI_STRICT\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/finn-b-E4_HYTHY_AI_STRICT\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Effect of RA on hyperthyroidism and hypothyroidism\u003c/h2\u003e\n \u003cp\u003eThe effects of each SNP in RA on hyperthyroidism and hypothyroidism can be found in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, B and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, B. The MR results indicated that there is a causal effect between RA on hyperthyroidism or hypothyroidism. The utilization of the IVW method to detect RA revealed an elevated relative risk of hyperthyroidism (OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI\u0026thinsp;=\u0026thinsp;1.17\u0026ndash;1.52, P\u0026thinsp;=\u0026thinsp;2.407e-05), as well as a heightened risk of hypothyroidism (OR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.21\u0026ndash;1.37, P\u0026thinsp;=\u0026thinsp;3.614e-16) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, B and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Afterward, Cochran\u0026apos;s Q test was employed to evaluate the robustness of the Mendelian randomization (MR) results,which revealed a significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, to mitigate heterogeneity, a random effects model was employed to estimate the effect size in the MR analysis. The findings from the MR-Egger intercept test indicated that the MR analysis was not influenced by potential horizontal pleiotropy (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05)(Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). n addition, we performed a \u0026quot;leave-one-out analysis\u0026quot; to assess the individual impact of each SNP on the overall effect of RA, hyperthyroidism, or hypothyroidism. The results indicated that the overall effect estimates of RA, hyperthyroidism, and hypothyroidism remain relatively stable and are not driven by any single SNP in the analysis(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Effect of hyperthyroidism and hypothyroidism on RA\u003c/h2\u003e\n \u003cp\u003eTo investigate the causal relationships between hyperthyroidism or hypothyroidism and RA, we conducted a reverse MR analysis. The analysis suggested that there might not be a causal relationship between hyperthyroidism and rheumatoid arthritis (IVW: P\u0026thinsp;=\u0026thinsp;0.769) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). On the flip side, it was observed that hypothyroidism might elevate the relative risk of developing rheumatoid arthritis (OR\u0026thinsp;=\u0026thinsp;1.57, 95% CI\u0026thinsp;=\u0026thinsp;1.30\u0026ndash;1.91, P\u0026thinsp;=\u0026thinsp;4.211e-06) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). For hypothyroidism, sensitivity analyses were conducted, and the findings indicated that the reverse MR analysis remained unaffected by any heterogeneity issues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). No evidence of pleiotropic effects was observed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Through the implementation of a \u0026ldquo;leave-one-out analysis\u0026rdquo;, we discovered that no individual SNP exhibited a significant independent impact on the susceptibility to hypothyroidism and rheumatoid arthritis(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eIn the MR analysis, we evaluated the effects of genetically predicted rheumatoid arthritis (RA) on the likelihood of developing hyperthyroidism and hypothyroidism.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of genetically predicted RA on the likelihood of developing hyperthyroidism and hypothyroidism in the MR analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enSNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMR methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehyperthyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.381e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u0026ndash;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.670e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u0026ndash;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.407e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u0026ndash;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.777e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u0026ndash;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.754e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u0026ndash;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.742e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u0026ndash;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.363e-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20\u0026ndash;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.614e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u0026ndash;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.318e-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u0026ndash;1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.450e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u0026ndash;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eMR, Mendelian randomization analysis; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted; WM, Weighted median; RA, Rheumatoid arthritis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSensitivity analysis of RA with hyperthyroidism and hypothyroidism by different analysis methods\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMR methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCochran Q statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHeterogeneity P\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePleiotropy P\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehyperthyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e266.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.721e-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e277.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.407e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e932.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.446e-145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e932.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.614e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eMR, Mendelian randomization analysis; IVW, Inverse variance weighted; RA, Rheumatoid arthritis\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of genetically predicted hyperthyroidism and hypothyroidism on the likelihood of developing RA in the MR analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enSNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMR methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehyperthyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u0026ndash;2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u0026ndash;1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u0026ndash;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.350e-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u0026ndash;3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.494e-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u0026ndash;145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.211e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30\u0026ndash;1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.320e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u0026ndash;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted\u0026nbsp;mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.802e-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u0026ndash;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eMR, Mendelian randomization analysis; SNP, Single nucleotide polymorphism; IVW, Inverse variance weighted; WM, Weighted median; RA, Rheumatoid arthritis\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSensitivity analysis of hyperthyroidism and hypothyroidism with RA by different analysis methods\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMR methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCochran Q statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHeterogeneity P\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePleiotropy P\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehyperthyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e970.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.043e-208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1077.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR\u0026nbsp;Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1196.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.697e-220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1254.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eMR, Mendelian randomization analysis; IVW, Inverse variance weighted; RA, Rheumatoid arthritis\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe MR method was utilized to explore the bidirectional causal association between RA and thyroid dysfunction. In the European population, we identified a reciprocal causal relationship between RA and hypothyroidism. Specifically, we found that the genetic predisposition to RA is linked to an elevated risk of hypothyroidism, and conversely, individuals with hypothyroidism have an increased susceptibility to RA. Furthermore, we observed a positive causal association between RA and hyperthyroidism. Nevertheless, the reverse MR analysis yielded inconclusive results, suggesting that there is no significant causal relationship between hyperthyroidism and RA.\u003c/p\u003e \u003cp\u003eFrom our observations in clinical practice, we have identified thyroid disease as a prevalent comorbidity in patients diagnosed with RA. Previous observational studies demonstrated that individuals with RA who also had hypothyroidism, increased disease activity and joint tenderness, and correction of hypothyroidism in individuals with RA can significantly improve the disease activity of patients with RA[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Likewise, a prospective cohort study revealed that female patients with RA had a three-fold higher incidence of hypothyroidism compared to the general population[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This co-occurrence of hypothyroidism in female RA patients was found to be associated with a heightened risk of cardiovascular diseases (CVD)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Recently, a meta-analysis from China determined that individuals diagnosed with RA exhibited a heightened susceptibility to developing thyroid disorders, especially hypothyroidism, which was 2.25 times higher than non-RA patients; And the increased risk of hyperthyroidism was only secondary, which was 1.65 times higher than that of non-RA patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, these studies could not explain a causal effect between RA and thyroid dysfunction, they provided sufficient evidence for an association between RA and hyperthyroidism and hypothyroidism. Through Mr.'s research, we have demonstrated that RA may increase the incidence of hyperthyroidism and hypothyroidism, and hypothyroidism may also increase the incidence of RA, which strengthens and enriches the findings of these previous observational investigations.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) employs genetic variations that have strong associations with a specific exposure as instrumental variables to assess the causal effects of the exposure on diverse study outcomes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our MR study, we initially chose SNPs that exhibited strong associations with the exposure factors. The selection of these SNPs was based on the three fundamental hypotheses of association, independence, and exclusivity. Five different analysis methods are used for MR analysis. The utilization of the IVW method to detect RA revealed an elevated relative risk of hyperthyroidism (OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI\u0026thinsp;=\u0026thinsp;1.17\u0026ndash;1.52, P\u0026thinsp;=\u0026thinsp;2.407e-05), as well as a heightened risk of hypothyroidism (OR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.21\u0026ndash;1.37, P\u0026thinsp;=\u0026thinsp;3.614e-16). On the flip side, it was observed that hypothyroidism might also elevate the relative risk of developing rheumatoid arthritis (OR\u0026thinsp;=\u0026thinsp;1.57, 95% CI\u0026thinsp;=\u0026thinsp;1.30\u0026ndash;1.91, P\u0026thinsp;=\u0026thinsp;4.211e-06). The results deduced by the other four methods accorded with the IVW analysis method. We also performed a sensitivity analysis, and heterogeneity testing revealed significant heterogeneity between each instrumental variable. However, it does not affect the final analysis results. It is worth noting that the MR Egger method suggests that multiple IVs do not have horizontal pleiotropy, so the research results may be less affected by gene pleiotropy.Through the above methods, we ultimately consider that the genetic susceptibility of RA is linked to a higher chance of developing hypothyroidism, and vice versa. The genetic susceptibility of RA is linked to a higher chance of developing hyperthyroidism, but hyperthyroidism seems to have no causal effect on RA. Graves' disease (GD) is a prevalent cause of hyperthyroidism, and research has indicated that 16.7% of individuals with GD also have another autoimmune disease[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Among these cases, rheumatoid arthritis (RA) has a prevalence rate of 1.9%[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In an Asian population, a Mendelian randomization study revealed a reciprocal causal relationship between GD and RA[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Due to the possibility that Mendelian randomization analysis may be influenced by factors such as race or region, further research is needed on the causality between hyperthyroidism and RA based on different races or regions.\u003c/p\u003e \u003cp\u003eAdditionally, the association between RA and thyroid diseases may have some other potential pathogenic mechanisms. Hyperthyroidism and hypothyroidism are mainly caused by AITD. There exists a correlation between RA and autoimmune thyroid diseases (AITD), yet the underlying causes of their co-occurrence remain unclear. Genetic variations in the human leukocyte antigen (HLA) system, cytotoxic T lymphocyte-associated protein 4 (CTLA-4), and protein tyrosine phosphatase non-receptor 22 (PTPN22) genes have been found to be linked to an increased susceptibility to autoimmune thyroid diseases (AITD)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. CTLA4 plays a negative regulatory role in T lymphocyte immune response[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Common allelic variations in CTLA-4 expression levels are major determinants of susceptibility to autoimmune diseases such as autoimmune thyroid disease and RA[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. PTPN22 is associated with TCR signaling and participates in regulating CBL function in T cell receptor signaling pathways[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Mutations in this gene can alter TCR regulation and T cell activation, closely related to the increased risk of various autoimmune diseases, including RA and GD. Recent findings have indicated that the expression of CXCL10 significantly rises in serum and/or tissues of organ-specific autoimmune diseases like GD and RA[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The production of IFN-γand TNF-αin thyroid tissue may be linked to the recruitment of Th1 lymphocytes, triggering the release of CXCL10 by thyroid cells, binding to CXCR3 on the surface of Th1 lymphocytes, and promoting the recruitment of Th1 lymphocytes into the thyroid, secreting IFN-γ and TNF-α, thus forming an amplified feedback loop, resulting in the persistence of the disease[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Similarly, many chemokines, including CXCL10, are present in the joints of RA, promote lymphocyte recruitment into the synovium, and are involved in germinal center formation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, CXCL10 can be used as a therapeutic target to prevent immune cells from recruiting to tissues, which needs to be further studied, and provides a new therapeutic strategy for RA and thyroid disease comorbidity.\u003c/p\u003e \u003cp\u003eOur research has some advantages. Firstly, the present analysis employing MR is grounded in GWAS, leveraging genetic data for causal inference purposes. Compared with traditional epidemiological methods, MR reduces the impact of confounding factors, reverse causality, and ethical ethics. Secondly, we employed diverse approaches for Mendelian randomization analysis and sensitivity analysis to ensure the robustness and validity of the final outcomes. Significantly, we have established a reciprocal causal association between RA and hypothyroidism, offering novel perspectives for clinicians in terms of RA screening and management strategies.\u003c/p\u003e \u003cp\u003eCertainly, there are certain limitations in this study. Firstly, the inclusion of SNPs in this study was limited to European populations, thus requiring further confirmation to establish the causal relationship between RA and hyperthyroidism and hypothyroidism in other racial or regional contexts. Secondly, due to the limitation of GWAS pooled data, it is impossible to perform stratified analysis according to factors such as disease course and severity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe evidence we provide suggests that the genetic susceptibility of RA is linked to the increasing risk of hyperthyroidism and hypothyroidism. Conversely, it is possible that genetic predisposition to hypothyroidism is linked to a higher risk of developing RA, while there may not be a causal association between genetic susceptibility to hyperthyroidism and an increased risk of RA. As a result, it is crucial to enhance screening and management of thyroid disorders in individuals with RA to enhance patient prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study were exclusively sourced from publicly available databases. All the data mentioned can be accessed and freely downloaded from the provided website. As a result of the re-evaluation of previously gathered and published data, no further ethics approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the\u0026nbsp;the IEU database (https://gwas.mrcieu.ac.uk/).The data access links for rheumatoid arthritis (RA), hypothyroidism, and hyperthyroidism can be found in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was supported by the\u0026nbsp;\u003cstrong\u003eNatural Science Foundation of China\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e81873153，81273905).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunyang Sun was responsible for the research design and writing of the paper. Jingjing Xiao oversaw the collection and organization of data. Yu Wang conducted the analysis and visualization of the results. Dongchu He conducted the research quality assessment. All authors made significant contributions to the article and have approved the final submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We thank all those who contributed and collected data for this work. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAletaha D, Smolen JS. Diagnosis and Management of Rheumatoid Arthritis: A Review. JAMA. 2018;320(13):1360-1372. \u003c/li\u003e\n\u003cli\u003eLee DM, Weinblatt ME. Rheumatoid arthritis. Lancet. 2001;358(9285):903-911. \u003c/li\u003e\n\u003cli\u003eSmolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis [published correction appears in Lancet. 2016 Oct 22;388(10055):1984]. 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Autoimmune thyroid disorders and rheumatoid arthritis: A bidirectional interplay. Autoimmun Rev. 2020;19(6):102529. \u003c/li\u003e\n\u003cli\u003ePetrovsk\u0026aacute; N, Prajzlerov\u0026aacute; K, Vencovsk\u0026yacute; J, \u0026Scaron;enolt L, Filkov\u0026aacute; M. The pre-clinical phase of rheumatoid arthritis: From risk factors to prevention of arthritis. Autoimmun Rev. 2021;20(5):102797. \u003c/li\u003e\n\u003cli\u003eAntonelli A, Ferrari SM, Corrado A, Di Domenicantonio A, Fallahi P. Autoimmune thyroid disorders. Autoimmun Rev. 2015;14(2):174-180. \u003c/li\u003e\n\u003cli\u003ePrzygodzka M, Filipowicz-Sosnowska A. Prevalence of thyroid diseases and antithyroid antibodies in women with rheumatoid arthritis. Pol Arch Med Wewn. 2009;119(1-2):39-43.\u003c/li\u003e\n\u003cli\u003eBagherzadeh-Fard M, Yazdanifar MA, Aghaali M, Masoumi M. The prevalence of thyroid dysfunction and autoimmune thyroid disease in patients with rheumatoid arthritis. BMC Rheumatol. 2022;6(1):63. \u003c/li\u003e\n\u003cli\u003eTam V, Patel N, Turcotte M, Boss\u0026eacute; Y, Par\u0026eacute; G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet. 2019;20(8):467-484. \u003c/li\u003e\n\u003cli\u003eSkrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326(16):1614-1621. \u003c/li\u003e\n\u003cli\u003eSmith GD, Ebrahim S. \u0026apos;Mendelian randomization\u0026apos;: can genetic epidemiology contribute to understanding environmental determinants of disease?. Int J Epidemiol. 2003;32(1):1-22.\u003c/li\u003e\n\u003cli\u003eDavey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-R98. \u003c/li\u003e\n\u003cli\u003eDavies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. Published 2018 Jul 12. \u003c/li\u003e\n\u003cli\u003eHu MJ, Tan JS, Gao XJ, Yang JG, Yang YJ. Effect of Cheese Intake on Cardiovascular Diseases and Cardiovascular Biomarkers. Nutrients. 2022;14(14):2936. \u003c/li\u003e\n\u003cli\u003eStaley JR, Blackshaw J, Kamat MA, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016;32(20):3207-3209. \u003c/li\u003e\n\u003cli\u003eZhang D, Hu Y, Guo W, et al. Mendelian randomization study reveals a causal relationship between rheumatoid arthritis and risk for pre-eclampsia. Front Immunol. 2022;13:1080980. \u003c/li\u003e\n\u003cli\u003eBowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36(11):1783-1802. \u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512-525.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. \u003cem\u003eGenet \u003c/em\u003eEpidemiol. 2016;40(4):304-314. \u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method [published correction appears in Eur J Epidemiol. 2017 Jun 29;:]. Eur J Epidemiol. 2017;32(5):377-389. \u003c/li\u003e\n\u003cli\u003eChen C, Su L, Duan W, Zheng Y, Zhang D, Wang Y. Asthma and atopic dermatitis as risk factors for rheumatoid arthritis: a bidirectional mendelian randomization study. BMC Med Genomics. 2023;16(1):41. \u003c/li\u003e\n\u003cli\u003eZohaib A, Rasheed A, Mahmud TE, et al. Correlation of Hypothyroidism With Disease Activity Score-28 in Patients of Rheumatoid Arthritis. Cureus. 2022;14(6):e26382. \u003c/li\u003e\n\u003cli\u003eRaterman HG, van Halm VP, Voskuyl AE, Simsek S, Dijkmans BA, Nurmohamed MT. Rheumatoid arthritis is associated with a high prevalence of hypothyroidism that amplifies its cardiovascular risk. Ann Rheum Dis. 2008;67(2):229-232. \u003c/li\u003e\n\u003cli\u003eLiu YJ, Miao HB, Lin S, Chen Z. Association between rheumatoid arthritis and thyroid dysfunction: A meta-analysis and systematic review. Front Endocrinol (Lausanne). 2022;13:1015516. \u003c/li\u003e\n\u003cli\u003eEvans DM, Davey Smith G. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annu Rev Genomics Hum Genet. 2015;16:327-350. \u003c/li\u003e\n\u003cli\u003eFerrari SM, Fallahi P, Ruffilli I, et al. The association of other autoimmune diseases in patients with Graves\u0026apos; disease (with or without ophthalmopathy): Review of the literature and report of a large series. Autoimmun Rev. 2019;18(3):287-292. \u003c/li\u003e\n\u003cli\u003eWu D, Xian W, Hong S, Liu B, Xiao H, Li Y. Graves\u0026apos; Disease and Rheumatoid Arthritis: A Bidirectional Mendelian Randomization Study. Front Endocrinol (Lausanne). 2021;12:702482. Published 2021 Aug 17. \u003c/li\u003e\n\u003cli\u003eUeda H, Howson JM, Esposito L, et al. Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease. Nature. 2003;423(6939):506-511. \u003c/li\u003e\n\u003cli\u003eMcInnes IB, Schett G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet. 2017;389(10086):2328-2337. \u003c/li\u003e\n\u003cli\u003ePaillon N, Hivroz C. CTLA4 prohibits T cells from cross-dressing. J Exp Med.2023;220(7):e20230419. \u003c/li\u003e\n\u003cli\u003eAnderson W, Barahmand-Pour-Whitman F, Linsley PS, Cerosaletti K, Buckner JH, Rawlings DJ. \u003cem\u003ePTPN22\u003c/em\u003e R620W gene editing in T cells enhances low-avidity TCR responses. Elife. 2023;12:e81577. \u003c/li\u003e\n\u003cli\u003eAntonelli A, Ferrari SM, Giuggioli D, Ferrannini E, Ferri C, Fallahi P. Chemokine (C-X-C motif) ligand (CXCL)10 in autoimmune diseases. Autoimmun Rev. 2014;13(3):272-280. \u003c/li\u003e\n\u003cli\u003eFerrari SM, Paparo SR, Ragusa F, et al. Chemokines in thyroid autoimmunity. Best Pract Res Clin Endocrinol Metab. 2023;37(2):101773. \u003c/li\u003e\n\u003cli\u003eZhong H, Xu LL, Bai MX, Su Y. Beijing Da Xue Xue Bao Yi Xue Ban. 2021;53(6):1026-1031.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, Hyperthyroidism, Hypothyroidism, Genetics, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3032973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3032973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eGrowing evidence has shown that Rheumatoid arthritis (RA) is associated with \u0026nbsp;hyperthyroidism and hypothyroidism.However, the reciprocal cause-and-effect relationship among those three factors has not yet been substantiated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe conducted a two-sample Mendelian randomization (TSMR) study with bidirectional analysis. We selected specific single nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA), hyperthyroidism, and hypothyroidism as instrumental variables. Every single nucleotide polymorphism (SNP) was derived from a genome-wide association study conducted specifically on individuals of European ancestry. For this study, the primary approach utilized to estimate the reciprocal causal relationship between rheumatoid arthritis (RA) and hyperthyroidism or hypothyroidism was the inverse-variance weighting (IVW) method. Finally, the robustness of the results was tested using sensitivity analysis and pleiotropic test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe utilization of the IVW method to detect rheumatoid arthritis (RA) revealed an elevated relative risk of hyperthyroidism (OR=1.33, 95% CI=1.17-1.52, P=2.407e-05), as well as a heightened risk of hypothyroidism (OR=1.29, 95% CI: 1.21-1.37, P=3.614e-16). On the flip side, it was observed that hypothyroidism might also elevate the relative risk of developing rheumatoid arthritis (OR=1.57, 95% CI=1.30-1.91, P=4.211e-06). Nevertheless, the analysis using the inverse-variance weighting (IVW) method suggested that there might not be a causal relationship between hyperthyroidism and rheumatoid arthritis (IVW: P=0.769). Finally, a sensitivity analysis was performed to assess the reliability of the results, and it indicated that no pleiotropic effects were observed, further bolstering the validity of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThe findings of this study demonstrate a bidirectional causal relationship between genetic susceptibility to rheumatoid arthritis (RA) and an augmented risk of developing hypothyroidism, and vice versa. Moreover, this research establishes a positive causal relationship between genetic susceptibility to rheumatoid arthritis (RA) and an elevated risk of hyperthyroidism. However, it does not provide evidence to support a causal relationship between genetic susceptibility to hyperthyroidism and the development of RA.\u003c/p\u003e","manuscriptTitle":"Causal relationship between rheumatoid arthritis and thyroid dysfunction: A two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-11 14:07:45","doi":"10.21203/rs.3.rs-3032973/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2673214-d7cf-4eae-b239-87c1456d3998","owner":[],"postedDate":"July 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-18T02:29:22+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-11 14:07:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3032973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3032973","identity":"rs-3032973","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}